Development of whole-heart myocardial perfusion magnetic resonance imaging

Myocardial perfusion imaging is of huge importance for the detection of coronary artery disease (CAD), one of the leading causes of morbidity and mortality worldwide, as it can provide non-invasive detection at the early stages of the disease. Magnetic resonance imaging (MRI) can assess myocardial perfusion by capturing the rst-pass perfusion (FPP) of a gadolinium-based contrast agent (GBCA), which is now a well-established technique and compares well with other modalities. However, current MRI methods are restricted by their limited coverage of the left ventricle. Interest has therefore grown in 3D volumetric \whole-heart" FPP by MRI, although many challenges currently limit this. For this thesis, myocardial perfusion assessment in general, and 3D whole-heart FPP in particular, were reviewed in depth, alongside MRI techniques important for achieving 3D FPP. From this, a 3D `stack-of-stars' (SOS) FPP sequence was developed with the aim of addressing some current limitations. These included the breath-hold requirement during GBCA rst-pass, long 3D shot durations corrupted by cardiac motion, and a propensity for artefacts in FPP. Parallel imaging and compressed sensing were investigated for accelerating whole-heart FPP, with modi cations presented to potentially improve robustness to free-breathing. Novel sequences were developed that were capable of individually improving some current sequence limits, including spatial resolution and signal-to-noise ratio, although with some sacri ces. A nal 3D SOS FPP technique was developed and tested at stress during free-breathing examinations of CAD patients and healthy volunteers. This enabled the rst known detection of an inducible perfusion defect with a free-breathing, compressed sensing, 3D FPP sequence; however, further investigation into the diagnostic performance is required. Simulations were performed to analyse potential artefacts in 3D FPP, as well as to examine ways towards further optimisation of 3D SOS FPP. The nal chapter discusses some limitations of the work and proposes opportunities for further investigation.Open Acces

Computed tomography angiography for the interventional cardiologist

WOS:000339902400002In recent years, coronary CT angiography (CCTA) has become a widely adopted technique, not only due to its high diagnostic accuracy, but also to the fact that CCTA provides a comprehensive evaluation of the total (obstructive and non-obstructive) coronary atherosclerotic burden. More recently, this technique has become mature, with a large body of evidence addressing its prognostic validation. In addition, CT angiography has moved from the field of `imagers' and clinicians and entered the interventional cardiology arena, aiding in the planning of both coronary and structural heart interventions, being transcatheter aortic valve implantation one of its most successful examples. It is therefore of utmost importance that interventional cardiologists become familiar with image interpretation and up-to-date regarding several CTA features, taking advantage of this information in planning the procedure, ultimately leading to improvement in patient outcomes. On the other hand, the increasing use of CCTA as a gatekeeper for invasive coronary angiography is expected to lead to an increase in the ratio of interventional to diagnostic procedures and significant changes in the daily cath-lab routine. In a foreseeable future, cath-labs will probably offer an invasive procedure only to patients expected to undergo an intervention, perhaps becoming in this change true interventional-labs.publishersversionpublishe

Motion-Compensated Image Reconstruction for Magnetic Resonance (MR) Imaging and for Simultaneous Positron Emission Tomography/MR Imaging

In this work, novel algorithms for 4D (3D + respiratory) and 5D (3D + respiratory + cardiac) motion-compensated (MoCo) magnetic resonance (MR) and positron emission tomography (PET) image reconstruction were developed. The focus of all methods was set on short MR acquisition times. Therefore, respiratory and cardiac patient motion were estimated on the basis of strongly undersampled radial MR data employing joint motion estimation and MR image reconstruction. In case of simultaneous PET/MR acquisitions, motion information derived from MR was incorporated into the MoCo PET reconstruction. 4D respiratory MoCo MR image reconstructions with acquisition times of 40 s achieved an image quality comparable to standard motion handling approaches, which require one order of magnitude longer MR acquisition times. Respiratory MoCo PET images using 1 min of the MR acquisition time for motion estimation revealed improved PET image quality and quantification accuracy when compared to standard reconstruction methods. Additional compensation of cardiac motion resulted in increased image sharpness of MR and PET images in the heart region and enabled time-resolved 5D imaging allowing for reconstruction of any arbitrary combination of respiratory and cardiac motion phases. The proposed methods for MoCo image reconstruction may be integrated into clinical routine, reducing MR acquisition times for improved patient comfort and increasing the diagnostic value of MR and simultaneous PET/MR examinations of the thorax and abdomen

Deep learning for fast and robust medical image reconstruction and analysis

Medical imaging is an indispensable component of modern medical research as well as clinical practice. Nevertheless, imaging techniques such as magnetic resonance imaging (MRI) and computational tomography (CT) are costly and are less accessible to the majority of the world. To make medical devices more accessible, affordable and efficient, it is crucial to re-calibrate our current imaging paradigm for smarter imaging. In particular, as medical imaging techniques have highly structured forms in the way they acquire data, they provide us with an opportunity to optimise the imaging techniques holistically by leveraging data. The central theme of this thesis is to explore different opportunities where we can exploit data and deep learning to improve the way we extract information for better, faster and smarter imaging. This thesis explores three distinct problems. The first problem is the time-consuming nature of dynamic MR data acquisition and reconstruction. We propose deep learning methods for accelerated dynamic MR image reconstruction, resulting in up to 10-fold reduction in imaging time. The second problem is the redundancy in our current imaging pipeline. Traditionally, imaging pipeline treated acquisition, reconstruction and analysis as separate steps. However, we argue that one can approach them holistically and optimise the entire pipeline jointly for a specific target goal. To this end, we propose deep learning approaches for obtaining high fidelity cardiac MR segmentation directly from significantly undersampled data, greatly exceeding the undersampling limit for image reconstruction. The final part of this thesis tackles the problem of interpretability of the deep learning algorithms. We propose attention-models that can implicitly focus on salient regions in an image to improve accuracy for ultrasound scan plane detection and CT segmentation. More crucially, these models can provide explainability, which is a crucial stepping stone for the harmonisation of smart imaging and current clinical practice.Open Acces

Patient Risk-Minimizing Tube Current Modulation in X-Ray Computed Tomography

This dissertation proposes a patient-specific tube current modulation for computed tomography (CT) that minimizes the individual patient risk (riskTCM). Modern CT scanners use automatic exposure control (AEC) techniques including tube current modulation (TCM) to reduce the radiation dose delivered to the patient while maintaining image quality. Today's TCM implementations aim at minimizing the tube current-time (mAs) product as a surrogate for patient dose, which is why they are referred to as mAsTCM hereafter. However, the actual patient risk, e.g., in the form of risk measures such as the effective dose Deff representing the sensitivity of individual organs with respect to ionizing radiation, is not taken into account. In order to be able to optimize the effective dose Deff or another biologically meaningful measure, organ doses must be estimated before the actual CT scan in order to compute an optimized riskTCM curve. This can be achieved using a machine learning approach and based on these information, the new patient risk-minimizing TCM curve can be obtained. The proposed riskTCM algorithm was evaluated in a simulation study for circular scans and compared against the current gold standard method mAsTCM and to a constant tube current as well as an organ-specific tube current modulation technique. The results illustrate that all anatomical regions can benefit from riskTCM and a reduction of effective dose of up to 30% can be expected compared to mAsTCM. Furthermore, riskTCM was extended to a spiral trajectory that is commonly used in clinical routine and initial measurements with phantoms have been performed. The introduction of riskTCM into clinical practice would only require a software update since almost all CT systems are already capable of modulating the tube current

Improved outcome prediction in tetralogy of Fallot

Successful advances in cardiac surgery have led to a paradigm shift in the management of an expanding population of repaired tetralogy of Fallot (rTOF) patients. However, late morbidity and mortality have not been abolished, with patients vulnerable to arrhythmia and sudden death. Outcome prediction remains challenging, mandating the identification of novel sensitive and specific non-invasive biomarkers. Cardiac fibrosis in rTOF has been shown to correlate to adverse clinical features, and therefore merits further study, particularly with regards to interstitial fibrosis. Cardiac remodelling following surgical pulmonary valve replacement in patients with rTOF was investigated. Structural reverse remodelling was observed to occur immediately after surgery, followed by gradual biological remodelling. A proactive surgical approach before right ventricular (RV) end-systolic indexed volumes exceed 82ml/m2 confers optimal postoperative RV normalisation. Novel cardiovascular magnetic resonance T1 mapping techniques were developed and tested to improve identification of RV interstitial cardiac fibrosis in rTOF. Multi-echo imaging to separate fat from myocardium, combined with blood signal suppression is promising as a feasible method in saturation-recovery T1 mapping, but requires further technical study prior to clinical application and validation. The genomic signatures of the pathological RV in rTOF were investigated by next generation RNA sequencing. Differential gene expression was evident, and potential molecular determinants of fibrotic and restrictive phenotypes were ascertained. Ubiquitin C may have important functional implications as a ‘network hub’ gene in rTOF. Finally, the longitudinal predictive role of neurohormone expression in patients with rTOF was examined. Neurohormonal activation was confirmed in rTOF, with serum brain natriuretic peptide being prognostic for mortality and sustained arrhythmias during extended follow-up. In conclusion, this work reflects the complex interplay of candidate biomarkers in influencing clinical outcomes. Myocardial fibrosis in rTOF remains a key diagnostic and therapeutic target for improving risk stratification and ameliorating morbidity in the lifelong care of these individuals.Open Acces

Myocardial CT perfusion imaging for the detection of obstructive coronary artery disease: multisegment reconstruction does not improve diagnostic performance

Background: Multisegment reconstruction (MSR) was introduced to shorten the temporal reconstruction window of computed tomography (CT) and thereby reduce motion artefacts. We investigated whether MSR of myocardial CT perfusion (CTP) can improve diagnostic performance in detecting obstructive coronary artery disease (CAD) compared with halfscan reconstruction (HSR). Methods: A total of 134 patients (median age 65.7 years) with clinical indication for invasive coronary angiography and without cardiac surgery prospectively underwent static CTP. In 93 patients with multisegment acquisition, we retrospectively performed both MSR and HSR and searched both reconstructions for perfusion defects. Subgroups with known (n = 68) or suspected CAD (n = 25) and high heart rate (n = 30) were analysed. The area under the curve (AUC) was compared applying DeLong approach using >= 50% stenosis on invasive coronary angiography as reference standard. Results: Per-patient analysis revealed the overall AUC of MSR (0.65 [95% confidence interval 0.53, 0.78]) to be inferior to that of HSR (0.79 [0.69, 0.88]; p = 0.011). AUCs of MSR and HSR were similar in all subgroups analysed (known CAD 0.62 [0.45, 0.79] versus 0.72 [0.57, 0.86]; p = 0.157; suspected CAD 0.80 [0.63, 0.97] versus 0.89 [0.77, 1.00]; p = 0.243; high heart rate 0.46 [0.19, 0.73] versus 0.55 [0.33, 0.77]; p = 0.389). Median stress radiation dose was higher for MSR than for HSR (6.67 mSv versus 3.64 mSv, p < 0.001). Conclusions: MSR did not improve diagnostic performance of myocardial CTP imaging while increasing radiation dose compared with HSR